Overview

Dataset statistics

Number of variables17
Number of observations3218
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory392.7 B

Variable types

Categorical6
Numeric11

Warnings

id has a high cardinality: 3218 distinct values High cardinality
title has a high cardinality: 3133 distinct values High cardinality
all_artists has a high cardinality: 788 distinct values High cardinality
release_date has a high cardinality: 942 distinct values High cardinality
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
energy is highly correlated with loudnessHigh correlation
loudness is highly correlated with energyHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
id is uniformly distributed Uniform
title is uniformly distributed Uniform
id has unique values Unique
popularity has 366 (11.4%) zeros Zeros
key has 361 (11.2%) zeros Zeros
instrumentalness has 1802 (56.0%) zeros Zeros

Reproduction

Analysis started2021-10-09 22:35:31.347032
Analysis finished2021-10-09 22:35:49.183314
Duration17.84 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct3218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size248.4 KiB
4ckuS4Nj4FZ7i3Def3Br8W
 
1
0JlPOiJFrlFnC5QHcLPyPV
 
1
1j2l6jMz85PaXuDiF08Vcv
 
1
0pJPdSVti6cTM1Q6xYGmcf
 
1
5Il6Oe7lr5XM7A0cWbVQtr
 
1
Other values (3213)
3213 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters70796
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3218 ?
Unique (%)100.0%

Sample

1st row0Li1OuXXfi7950ilZUFGkF
2nd row4at3d5QWnlibMVN75ECDrp
3rd row6b2oQwSGFkzsMtQruIWm2p
4th row5fsjS6L83RSJBqRJKL0BTY
5th row7cvkXf3AwPGT041PyOi5VX

Common Values

ValueCountFrequency (%)
4ckuS4Nj4FZ7i3Def3Br8W1
 
< 0.1%
0JlPOiJFrlFnC5QHcLPyPV1
 
< 0.1%
1j2l6jMz85PaXuDiF08Vcv1
 
< 0.1%
0pJPdSVti6cTM1Q6xYGmcf1
 
< 0.1%
5Il6Oe7lr5XM7A0cWbVQtr1
 
< 0.1%
005lwxGU1tms6HGELIcUv91
 
< 0.1%
1Twabx98PlYV9aeYhyzHtP1
 
< 0.1%
2FpyKkCFIfqmp6eQx4Rn1V1
 
< 0.1%
7HKxTNVlkHsfMLhigmhC0I1
 
< 0.1%
0nJW01T7XtvILxQgC5J7Wh1
 
< 0.1%
Other values (3208)3208
99.7%

Length

2021-10-09T18:35:49.371475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5kbarwixemgkvuax9vtvc91
 
< 0.1%
66q3famsx5ehamgbka9alp1
 
< 0.1%
5juu0una8vwhtz9lkmwuvi1
 
< 0.1%
3dk6m7it6pw857fcqftmds1
 
< 0.1%
6hfywc5eqyrryhytatcb5y1
 
< 0.1%
5woaf1b5xienwfmb5nzikf1
 
< 0.1%
7nyvutkqmx1v80s2fh2s9j1
 
< 0.1%
3u21a07galocc4p7j8rxcn1
 
< 0.1%
0ws8d3ewudgy962xftb0h51
 
< 0.1%
4mxhiyirdmgauvzc5iftwc1
 
< 0.1%
Other values (3208)3208
99.7%

Most occurring characters

ValueCountFrequency (%)
61593
 
2.3%
01523
 
2.2%
11523
 
2.2%
41505
 
2.1%
31476
 
2.1%
71440
 
2.0%
21431
 
2.0%
51428
 
2.0%
E1198
 
1.7%
F1195
 
1.7%
Other values (52)56484
79.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter28503
40.3%
Lowercase Letter28164
39.8%
Decimal Number14129
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E1198
 
4.2%
F1195
 
4.2%
A1138
 
4.0%
L1135
 
4.0%
J1132
 
4.0%
O1128
 
4.0%
C1113
 
3.9%
Q1111
 
3.9%
D1111
 
3.9%
Z1108
 
3.9%
Other values (16)17134
60.1%
Lowercase Letter
ValueCountFrequency (%)
t1147
 
4.1%
r1146
 
4.1%
w1130
 
4.0%
k1118
 
4.0%
p1110
 
3.9%
o1110
 
3.9%
b1107
 
3.9%
d1106
 
3.9%
a1102
 
3.9%
n1094
 
3.9%
Other values (16)16994
60.3%
Decimal Number
ValueCountFrequency (%)
61593
11.3%
01523
10.8%
11523
10.8%
41505
10.7%
31476
10.4%
71440
10.2%
21431
10.1%
51428
10.1%
81111
7.9%
91099
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin56667
80.0%
Common14129
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E1198
 
2.1%
F1195
 
2.1%
t1147
 
2.0%
r1146
 
2.0%
A1138
 
2.0%
L1135
 
2.0%
J1132
 
2.0%
w1130
 
2.0%
O1128
 
2.0%
k1118
 
2.0%
Other values (42)45200
79.8%
Common
ValueCountFrequency (%)
61593
11.3%
01523
10.8%
11523
10.8%
41505
10.7%
31476
10.4%
71440
10.2%
21431
10.1%
51428
10.1%
81111
7.9%
91099
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII70796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61593
 
2.3%
01523
 
2.2%
11523
 
2.2%
41505
 
2.1%
31476
 
2.1%
71440
 
2.0%
21431
 
2.0%
51428
 
2.0%
E1198
 
1.7%
F1195
 
1.7%
Other values (52)56484
79.8%

title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3133
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
Stay
 
4
Let It Go
 
4
Pony
 
3
Up All Night
 
3
Sorry
 
3
Other values (3128)
3201 

Length

Max length116
Median length13
Mean length17.94965817
Min length1

Characters and Unicode

Total characters57762
Distinct characters98
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3055 ?
Unique (%)94.9%

Sample

1st rowCardigan
2nd rowPretty Little Fears (feat. J. Cole)
3rd rowCreep
4th rowGot Your Money (feat. Kelis)
5th rowDance for You

Common Values

ValueCountFrequency (%)
Stay4
 
0.1%
Let It Go4
 
0.1%
Pony3
 
0.1%
Up All Night3
 
0.1%
Sorry3
 
0.1%
Yikes2
 
0.1%
Wings2
 
0.1%
Lonely2
 
0.1%
You & Me - Flume Remix2
 
0.1%
Glitter2
 
0.1%
Other values (3123)3191
99.2%

Length

2021-10-09T18:35:49.614684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
feat683
 
6.3%
438
 
4.0%
the249
 
2.3%
me142
 
1.3%
you138
 
1.3%
it127
 
1.2%
i122
 
1.1%
a100
 
0.9%
love96
 
0.9%
with92
 
0.8%
Other values (3203)8699
79.9%

Most occurring characters

ValueCountFrequency (%)
7668
 
13.3%
e5125
 
8.9%
a3620
 
6.3%
t2949
 
5.1%
o2949
 
5.1%
i2676
 
4.6%
n2433
 
4.2%
r2068
 
3.6%
l1651
 
2.9%
s1604
 
2.8%
Other values (88)25019
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35510
61.5%
Uppercase Letter10344
 
17.9%
Space Separator7668
 
13.3%
Other Punctuation1649
 
2.9%
Open Punctuation837
 
1.4%
Close Punctuation837
 
1.4%
Decimal Number621
 
1.1%
Dash Punctuation218
 
0.4%
Currency Symbol41
 
0.1%
Final Punctuation15
 
< 0.1%
Other values (4)22
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5125
14.4%
a3620
 
10.2%
t2949
 
8.3%
o2949
 
8.3%
i2676
 
7.5%
n2433
 
6.9%
r2068
 
5.8%
l1651
 
4.6%
s1604
 
4.5%
h1326
 
3.7%
Other values (22)9109
25.7%
Uppercase Letter
ValueCountFrequency (%)
T868
 
8.4%
S823
 
8.0%
B690
 
6.7%
M686
 
6.6%
L617
 
6.0%
I539
 
5.2%
A501
 
4.8%
R494
 
4.8%
D489
 
4.7%
W488
 
4.7%
Other values (18)4149
40.1%
Other Punctuation
ValueCountFrequency (%)
.916
55.5%
&244
 
14.8%
'217
 
13.2%
,145
 
8.8%
?28
 
1.7%
"27
 
1.6%
/26
 
1.6%
!21
 
1.3%
*14
 
0.8%
:5
 
0.3%
Other values (3)6
 
0.4%
Decimal Number
ValueCountFrequency (%)
0174
28.0%
2136
21.9%
1113
18.2%
442
 
6.8%
335
 
5.6%
931
 
5.0%
530
 
4.8%
628
 
4.5%
716
 
2.6%
816
 
2.6%
Open Punctuation
ValueCountFrequency (%)
(827
98.8%
[10
 
1.2%
Close Punctuation
ValueCountFrequency (%)
)827
98.8%
]10
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-217
99.5%
1
 
0.5%
Final Punctuation
ValueCountFrequency (%)
13
86.7%
2
 
13.3%
Math Symbol
ValueCountFrequency (%)
|8
72.7%
+3
 
27.3%
Space Separator
ValueCountFrequency (%)
7668
100.0%
Currency Symbol
ValueCountFrequency (%)
$41
100.0%
Connector Punctuation
ValueCountFrequency (%)
_8
100.0%
Other Symbol
ValueCountFrequency (%)
®1
100.0%
Initial Punctuation
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45854
79.4%
Common11908
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5125
 
11.2%
a3620
 
7.9%
t2949
 
6.4%
o2949
 
6.4%
i2676
 
5.8%
n2433
 
5.3%
r2068
 
4.5%
l1651
 
3.6%
s1604
 
3.5%
h1326
 
2.9%
Other values (50)19453
42.4%
Common
ValueCountFrequency (%)
7668
64.4%
.916
 
7.7%
(827
 
6.9%
)827
 
6.9%
&244
 
2.0%
'217
 
1.8%
-217
 
1.8%
0174
 
1.5%
,145
 
1.2%
2136
 
1.1%
Other values (28)537
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII57723
99.9%
Latin 1 Sup21
 
< 0.1%
Punctuation18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7668
 
13.3%
e5125
 
8.9%
a3620
 
6.3%
t2949
 
5.1%
o2949
 
5.1%
i2676
 
4.6%
n2433
 
4.2%
r2068
 
3.6%
l1651
 
2.9%
s1604
 
2.8%
Other values (75)24980
43.3%
Punctuation
ValueCountFrequency (%)
13
72.2%
2
 
11.1%
2
 
11.1%
1
 
5.6%
Latin 1 Sup
ValueCountFrequency (%)
é9
42.9%
á3
 
14.3%
Ü2
 
9.5%
ñ2
 
9.5%
í1
 
4.8%
®1
 
4.8%
à1
 
4.8%
ü1
 
4.8%
Í1
 
4.8%

all_artists
Categorical

HIGH CARDINALITY

Distinct788
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Drake
 
88
Ariana Grande
 
68
Travis Scott
 
58
The Weeknd
 
54
Rihanna
 
46
Other values (783)
2904 

Length

Max length38
Median length10
Mean length9.931945308
Min length2

Characters and Unicode

Total characters31961
Distinct characters77
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)14.1%

Sample

1st rowDon Toliver
2nd rowJ. Cole
3rd rowRadiohead
4th rowKelis
5th rowBeyoncé

Common Values

ValueCountFrequency (%)
Drake88
 
2.7%
Ariana Grande68
 
2.1%
Travis Scott58
 
1.8%
The Weeknd54
 
1.7%
Rihanna46
 
1.4%
Lil Uzi Vert42
 
1.3%
Gunna41
 
1.3%
Young Thug38
 
1.2%
One Direction38
 
1.2%
J. Cole37
 
1.1%
Other values (778)2708
84.2%

Length

2021-10-09T18:35:49.860895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the204
 
3.5%
lil146
 
2.5%
drake88
 
1.5%
ariana68
 
1.2%
grande68
 
1.2%
travis60
 
1.0%
scott58
 
1.0%
young56
 
1.0%
weeknd54
 
0.9%
rihanna46
 
0.8%
Other values (1164)4972
85.4%

Most occurring characters

ValueCountFrequency (%)
e2871
 
9.0%
2602
 
8.1%
a2582
 
8.1%
i2179
 
6.8%
n1770
 
5.5%
r1670
 
5.2%
o1430
 
4.5%
l1284
 
4.0%
t938
 
2.9%
h855
 
2.7%
Other values (67)13780
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21751
68.1%
Uppercase Letter7032
 
22.0%
Space Separator2602
 
8.1%
Other Punctuation241
 
0.8%
Decimal Number223
 
0.7%
Dash Punctuation57
 
0.2%
Currency Symbol47
 
0.1%
Math Symbol5
 
< 0.1%
Final Punctuation2
 
< 0.1%
Other Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2871
13.2%
a2582
11.9%
i2179
10.0%
n1770
 
8.1%
r1670
 
7.7%
o1430
 
6.6%
l1284
 
5.9%
t938
 
4.3%
h855
 
3.9%
s848
 
3.9%
Other values (19)5324
24.5%
Uppercase Letter
ValueCountFrequency (%)
T710
 
10.1%
S564
 
8.0%
B556
 
7.9%
M445
 
6.3%
A433
 
6.2%
C396
 
5.6%
L383
 
5.4%
D359
 
5.1%
R338
 
4.8%
K289
 
4.1%
Other values (17)2559
36.4%
Decimal Number
ValueCountFrequency (%)
261
27.4%
037
16.6%
136
16.1%
522
 
9.9%
621
 
9.4%
820
 
9.0%
49
 
4.0%
39
 
4.0%
97
 
3.1%
71
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.160
66.4%
'28
 
11.6%
&21
 
8.7%
,18
 
7.5%
!14
 
5.8%
Space Separator
ValueCountFrequency (%)
2602
100.0%
Dash Punctuation
ValueCountFrequency (%)
-57
100.0%
Currency Symbol
ValueCountFrequency (%)
$47
100.0%
Math Symbol
ValueCountFrequency (%)
+5
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Other Symbol
ValueCountFrequency (%)
®1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin28783
90.1%
Common3178
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2871
 
10.0%
a2582
 
9.0%
i2179
 
7.6%
n1770
 
6.1%
r1670
 
5.8%
o1430
 
5.0%
l1284
 
4.5%
t938
 
3.3%
h855
 
3.0%
s848
 
2.9%
Other values (46)12356
42.9%
Common
ValueCountFrequency (%)
2602
81.9%
.160
 
5.0%
261
 
1.9%
-57
 
1.8%
$47
 
1.5%
037
 
1.2%
136
 
1.1%
'28
 
0.9%
522
 
0.7%
621
 
0.7%
Other values (11)107
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII31892
99.8%
Latin 1 Sup67
 
0.2%
Punctuation2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2871
 
9.0%
2602
 
8.2%
a2582
 
8.1%
i2179
 
6.8%
n1770
 
5.5%
r1670
 
5.2%
o1430
 
4.5%
l1284
 
4.0%
t938
 
2.9%
h855
 
2.7%
Other values (61)13711
43.0%
Latin 1 Sup
ValueCountFrequency (%)
é62
92.5%
á2
 
3.0%
®1
 
1.5%
Í1
 
1.5%
ü1
 
1.5%
Punctuation
ValueCountFrequency (%)
2
100.0%

popularity
Real number (ℝ≥0)

ZEROS

Distinct88
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.03076445
Minimum0
Maximum88
Zeros366
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:49.975994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median59
Q369
95-th percentile78.15
Maximum88
Range88
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.86984156
Coefficient of variation (CV)0.4587639988
Kurtosis0.2919976412
Mean52.03076445
Median Absolute Deviation (MAD)11
Skewness-1.15104332
Sum167435
Variance569.7693361
MonotonicityNot monotonic
2021-10-09T18:35:50.079583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0366
 
11.4%
6789
 
2.8%
7088
 
2.7%
6486
 
2.7%
6285
 
2.6%
7384
 
2.6%
6683
 
2.6%
5581
 
2.5%
6080
 
2.5%
5777
 
2.4%
Other values (78)2099
65.2%
ValueCountFrequency (%)
0366
11.4%
129
 
0.9%
26
 
0.2%
37
 
0.2%
44
 
0.1%
55
 
0.2%
64
 
0.1%
74
 
0.1%
82
 
0.1%
92
 
0.1%
ValueCountFrequency (%)
883
 
0.1%
872
 
0.1%
858
 
0.2%
8411
 
0.3%
8319
0.6%
8219
0.6%
8127
0.8%
8036
1.1%
7936
1.1%
7837
1.1%

release_date
Categorical

HIGH CARDINALITY

Distinct942
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size210.2 KiB
2013-01-01
 
61
2018-08-03
 
37
2019-08-16
 
31
2020-03-13
 
27
2018-03-30
 
26
Other values (937)
3036 

Length

Max length10
Median length10
Mean length9.84617775
Min length4

Characters and Unicode

Total characters31685
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique504 ?
Unique (%)15.7%

Sample

1st row2020-03-13
2nd row2018-09-14
3rd row1993-02-22
4th row2008
5th row2011-06-24

Common Values

ValueCountFrequency (%)
2013-01-0161
 
1.9%
2018-08-0337
 
1.1%
2019-08-1631
 
1.0%
2020-03-1327
 
0.8%
2018-03-3026
 
0.8%
2017-08-2526
 
0.8%
2012-01-0126
 
0.8%
2019-11-2224
 
0.7%
2017-06-0923
 
0.7%
2017-07-2122
 
0.7%
Other values (932)2915
90.6%

Length

2021-10-09T18:35:50.305276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-01-0161
 
1.9%
2018-08-0337
 
1.1%
2019-08-1631
 
1.0%
2020-03-1327
 
0.8%
2018-03-3026
 
0.8%
2017-08-2526
 
0.8%
2012-01-0126
 
0.8%
2019-11-2224
 
0.7%
2017-06-0923
 
0.7%
2017-07-2122
 
0.7%
Other values (932)2915
90.6%

Most occurring characters

ValueCountFrequency (%)
07569
23.9%
-6271
19.8%
15860
18.5%
25058
16.0%
91389
 
4.4%
81109
 
3.5%
6974
 
3.1%
7949
 
3.0%
3926
 
2.9%
5800
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25414
80.2%
Dash Punctuation6271
 
19.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07569
29.8%
15860
23.1%
25058
19.9%
91389
 
5.5%
81109
 
4.4%
6974
 
3.8%
7949
 
3.7%
3926
 
3.6%
5800
 
3.1%
4780
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
-6271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common31685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07569
23.9%
-6271
19.8%
15860
18.5%
25058
16.0%
91389
 
4.4%
81109
 
3.5%
6974
 
3.1%
7949
 
3.0%
3926
 
2.9%
5800
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII31685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07569
23.9%
-6271
19.8%
15860
18.5%
25058
16.0%
91389
 
4.4%
81109
 
3.5%
6974
 
3.1%
7949
 
3.0%
3926
 
2.9%
5800
 
2.5%

danceability
Real number (ℝ≥0)

Distinct648
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6924045991
Minimum0.153
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:50.408866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.153
5-th percentile0.415
Q10.58525
median0.707
Q30.813
95-th percentile0.92
Maximum0.986
Range0.833
Interquartile range (IQR)0.22775

Descriptive statistics

Standard deviation0.1545261905
Coefficient of variation (CV)0.2231732584
Kurtosis-0.3910986908
Mean0.6924045991
Median Absolute Deviation (MAD)0.114
Skewness-0.4338215639
Sum2228.158
Variance0.02387834355
MonotonicityNot monotonic
2021-10-09T18:35:50.516458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67815
 
0.5%
0.7613
 
0.4%
0.70613
 
0.4%
0.73413
 
0.4%
0.813
 
0.4%
0.67512
 
0.4%
0.66912
 
0.4%
0.83712
 
0.4%
0.62412
 
0.4%
0.55912
 
0.4%
Other values (638)3091
96.1%
ValueCountFrequency (%)
0.1531
< 0.1%
0.181
< 0.1%
0.1981
< 0.1%
0.2281
< 0.1%
0.2321
< 0.1%
0.2451
< 0.1%
0.2482
0.1%
0.251
< 0.1%
0.2562
0.1%
0.2641
< 0.1%
ValueCountFrequency (%)
0.9861
< 0.1%
0.9791
< 0.1%
0.9771
< 0.1%
0.9751
< 0.1%
0.9741
< 0.1%
0.9722
0.1%
0.9712
0.1%
0.972
0.1%
0.9681
< 0.1%
0.9671
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct731
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6071668428
Minimum0.0307
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:50.629555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0307
5-th percentile0.31685
Q10.493
median0.6125
Q30.728
95-th percentile0.869
Maximum0.989
Range0.9583
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.168668181
Coefficient of variation (CV)0.2777954412
Kurtosis-0.1313257572
Mean0.6071668428
Median Absolute Deviation (MAD)0.1165
Skewness-0.3121439752
Sum1953.8629
Variance0.02844895527
MonotonicityNot monotonic
2021-10-09T18:35:50.746155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.66215
 
0.5%
0.64815
 
0.5%
0.71214
 
0.4%
0.57313
 
0.4%
0.71513
 
0.4%
0.72813
 
0.4%
0.55413
 
0.4%
0.70313
 
0.4%
0.62313
 
0.4%
0.52213
 
0.4%
Other values (721)3083
95.8%
ValueCountFrequency (%)
0.03071
< 0.1%
0.05611
< 0.1%
0.06131
< 0.1%
0.06621
< 0.1%
0.06711
< 0.1%
0.08091
< 0.1%
0.08511
< 0.1%
0.09241
< 0.1%
0.09291
< 0.1%
0.09521
< 0.1%
ValueCountFrequency (%)
0.9891
< 0.1%
0.9882
0.1%
0.9871
< 0.1%
0.9841
< 0.1%
0.9811
< 0.1%
0.9751
< 0.1%
0.9741
< 0.1%
0.971
< 0.1%
0.9661
< 0.1%
0.9631
< 0.1%

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.049409571
Minimum0
Maximum11
Zeros361
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:51.062927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.657514859
Coefficient of variation (CV)0.7243450561
Kurtosis-1.359746896
Mean5.049409571
Median Absolute Deviation (MAD)4
Skewness0.09300196782
Sum16249
Variance13.37741495
MonotonicityNot monotonic
2021-10-09T18:35:51.135989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1542
16.8%
0361
11.2%
7306
9.5%
8276
8.6%
2259
8.0%
11257
8.0%
5246
7.6%
6237
7.4%
10228
7.1%
9226
7.0%
Other values (2)280
8.7%
ValueCountFrequency (%)
0361
11.2%
1542
16.8%
2259
8.0%
374
 
2.3%
4206
 
6.4%
5246
7.6%
6237
7.4%
7306
9.5%
8276
8.6%
9226
7.0%
ValueCountFrequency (%)
11257
8.0%
10228
7.1%
9226
7.0%
8276
8.6%
7306
9.5%
6237
7.4%
5246
7.6%
4206
6.4%
374
 
2.3%
2259
8.0%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2662
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.908602237
Minimum-23.023
Maximum-0.656
Zeros0
Zeros (%)0.0%
Negative3218
Negative (%)100.0%
Memory size25.3 KiB
2021-10-09T18:35:51.229569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-23.023
5-th percentile-11.5489
Q1-8.23775
median-6.559
Q3-5.153
95-th percentile-3.4158
Maximum-0.656
Range22.367
Interquartile range (IQR)3.08475

Descriptive statistics

Standard deviation2.606645594
Coefficient of variation (CV)-0.377304338
Kurtosis2.943941194
Mean-6.908602237
Median Absolute Deviation (MAD)1.5295
Skewness-1.157891681
Sum-22231.882
Variance6.794601251
MonotonicityNot monotonic
2021-10-09T18:35:51.324151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.2734
 
0.1%
-6.7574
 
0.1%
-5.9464
 
0.1%
-7.2474
 
0.1%
-6.0624
 
0.1%
-4.244
 
0.1%
-4.9934
 
0.1%
-7.9093
 
0.1%
-8.9573
 
0.1%
-5.2213
 
0.1%
Other values (2652)3181
98.9%
ValueCountFrequency (%)
-23.0231
< 0.1%
-22.6091
< 0.1%
-22.321
< 0.1%
-21.8771
< 0.1%
-21.491
< 0.1%
-19.3461
< 0.1%
-18.4351
< 0.1%
-18.1411
< 0.1%
-17.9491
< 0.1%
-17.9041
< 0.1%
ValueCountFrequency (%)
-0.6561
< 0.1%
-1.2181
< 0.1%
-1.2991
< 0.1%
-1.3041
< 0.1%
-1.3571
< 0.1%
-1.4391
< 0.1%
-1.5381
< 0.1%
-1.5411
< 0.1%
-1.6381
< 0.1%
-1.691
< 0.1%

mode
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size182.4 KiB
1
1842 
0
1376 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3218
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11842
57.2%
01376
42.8%

Length

2021-10-09T18:35:51.499801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-09T18:35:51.555348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11842
57.2%
01376
42.8%

Most occurring characters

ValueCountFrequency (%)
11842
57.2%
01376
42.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3218
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11842
57.2%
01376
42.8%

Most occurring scripts

ValueCountFrequency (%)
Common3218
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11842
57.2%
01376
42.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11842
57.2%
01376
42.8%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1604
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.210370666
Minimum2.64 × 10-5
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:51.625910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.64 × 10-5
5-th percentile0.002677
Q10.0321
median0.112
Q30.308
95-th percentile0.75815
Maximum0.991
Range0.9909736
Interquartile range (IQR)0.2759

Descriptive statistics

Standard deviation0.238825527
Coefficient of variation (CV)1.135260593
Kurtosis1.153707013
Mean0.210370666
Median Absolute Deviation (MAD)0.0969
Skewness1.418931987
Sum676.9728032
Variance0.05703763237
MonotonicityNot monotonic
2021-10-09T18:35:51.733001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14513
 
0.4%
0.10512
 
0.4%
0.11112
 
0.4%
0.11512
 
0.4%
0.10711
 
0.3%
0.17211
 
0.3%
0.10111
 
0.3%
0.1359
 
0.3%
0.229
 
0.3%
0.01759
 
0.3%
Other values (1594)3109
96.6%
ValueCountFrequency (%)
2.64 × 10-51
< 0.1%
4.62 × 10-51
< 0.1%
6.48 × 10-51
< 0.1%
8.05 × 10-51
< 0.1%
9.43 × 10-51
< 0.1%
0.0001071
< 0.1%
0.0001311
< 0.1%
0.0001361
< 0.1%
0.0001381
< 0.1%
0.0001431
< 0.1%
ValueCountFrequency (%)
0.9911
< 0.1%
0.9832
0.1%
0.9821
< 0.1%
0.9781
< 0.1%
0.9761
< 0.1%
0.9752
0.1%
0.9741
< 0.1%
0.9711
< 0.1%
0.9631
< 0.1%
0.9621
< 0.1%

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct1127
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01167211259
Minimum0
Maximum0.973
Zeros1802
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:51.840593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.6 × 10-5
95-th percentile0.02052
Maximum0.973
Range0.973
Interquartile range (IQR)4.6 × 10-5

Descriptive statistics

Standard deviation0.07262849226
Coefficient of variation (CV)6.222394766
Kurtosis81.71600316
Mean0.01167211259
Median Absolute Deviation (MAD)0
Skewness8.570874553
Sum37.5608583
Variance0.005274897888
MonotonicityNot monotonic
2021-10-09T18:35:51.947185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01802
56.0%
1.21 × 10-66
 
0.2%
1.04 × 10-65
 
0.2%
2.02 × 10-54
 
0.1%
1.58 × 10-64
 
0.1%
0.0001154
 
0.1%
1.22 × 10-54
 
0.1%
1.16 × 10-64
 
0.1%
0.0001254
 
0.1%
0.0001944
 
0.1%
Other values (1117)1377
42.8%
ValueCountFrequency (%)
01802
56.0%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
0.1%
1.02 × 10-62
 
0.1%
1.03 × 10-61
 
< 0.1%
1.04 × 10-65
 
0.2%
1.05 × 10-61
 
< 0.1%
1.06 × 10-61
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.1 × 10-62
 
0.1%
ValueCountFrequency (%)
0.9731
< 0.1%
0.9241
< 0.1%
0.9091
< 0.1%
0.861
< 0.1%
0.8472
0.1%
0.7741
< 0.1%
0.7581
< 0.1%
0.7441
< 0.1%
0.7421
< 0.1%
0.741
< 0.1%

liveness
Real number (ℝ≥0)

Distinct886
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1819577999
Minimum0.021
Maximum0.977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:52.054277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.021
5-th percentile0.061485
Q10.0986
median0.126
Q30.22175
95-th percentile0.45215
Maximum0.977
Range0.956
Interquartile range (IQR)0.12315

Descriptive statistics

Standard deviation0.1369296136
Coefficient of variation (CV)0.7525350037
Kurtosis4.926114949
Mean0.1819577999
Median Absolute Deviation (MAD)0.0408
Skewness2.078094292
Sum585.5402
Variance0.01874971908
MonotonicityNot monotonic
2021-10-09T18:35:52.161369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10445
 
1.4%
0.11142
 
1.3%
0.10940
 
1.2%
0.10739
 
1.2%
0.10838
 
1.2%
0.11537
 
1.1%
0.10637
 
1.1%
0.1136
 
1.1%
0.10135
 
1.1%
0.11435
 
1.1%
Other values (876)2834
88.1%
ValueCountFrequency (%)
0.0211
< 0.1%
0.02151
< 0.1%
0.02231
< 0.1%
0.02241
< 0.1%
0.02411
< 0.1%
0.02431
< 0.1%
0.02481
< 0.1%
0.02681
< 0.1%
0.02861
< 0.1%
0.02881
< 0.1%
ValueCountFrequency (%)
0.9771
< 0.1%
0.9221
< 0.1%
0.8731
< 0.1%
0.8431
< 0.1%
0.8361
< 0.1%
0.8331
< 0.1%
0.8261
< 0.1%
0.8231
< 0.1%
0.8181
< 0.1%
0.8171
< 0.1%

valence
Real number (ℝ≥0)

Distinct907
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4659381603
Minimum0.0333
Maximum0.991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:52.267460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0333
5-th percentile0.121
Q10.285
median0.4475
Q30.641
95-th percentile0.85715
Maximum0.991
Range0.9577
Interquartile range (IQR)0.356

Descriptive statistics

Standard deviation0.2269325328
Coefficient of variation (CV)0.4870443164
Kurtosis-0.8431206768
Mean0.4659381603
Median Absolute Deviation (MAD)0.1755
Skewness0.2157784335
Sum1499.389
Variance0.05149837445
MonotonicityNot monotonic
2021-10-09T18:35:52.372049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.40512
 
0.4%
0.29111
 
0.3%
0.43411
 
0.3%
0.42511
 
0.3%
0.54210
 
0.3%
0.34910
 
0.3%
0.32910
 
0.3%
0.43510
 
0.3%
0.39310
 
0.3%
0.3410
 
0.3%
Other values (897)3113
96.7%
ValueCountFrequency (%)
0.03331
< 0.1%
0.03611
< 0.1%
0.03731
< 0.1%
0.03761
< 0.1%
0.0392
0.1%
0.03921
< 0.1%
0.03941
< 0.1%
0.03951
< 0.1%
0.03961
< 0.1%
0.03972
0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.9791
 
< 0.1%
0.9781
 
< 0.1%
0.9741
 
< 0.1%
0.9721
 
< 0.1%
0.973
0.1%
0.9691
 
< 0.1%
0.9681
 
< 0.1%
0.9671
 
< 0.1%
0.9663
0.1%

tempo
Real number (ℝ≥0)

Distinct3031
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.0155942
Minimum55.513
Maximum215.669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:52.476139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum55.513
5-th percentile78.0312
Q196.058
median117.5325
Q3139.8815
95-th percentile171.08885
Maximum215.669
Range160.156
Interquartile range (IQR)43.8235

Descriptive statistics

Standard deviation28.6571198
Coefficient of variation (CV)0.2407845796
Kurtosis-0.3386327287
Mean119.0155942
Median Absolute Deviation (MAD)21.5315
Skewness0.4647690631
Sum382992.182
Variance821.2305152
MonotonicityNot monotonic
2021-10-09T18:35:52.576725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.0224
 
0.1%
100.0214
 
0.1%
129.9934
 
0.1%
96.0383
 
0.1%
97.9723
 
0.1%
129.9643
 
0.1%
122.0033
 
0.1%
95.0023
 
0.1%
101.9933
 
0.1%
100.0493
 
0.1%
Other values (3021)3185
99.0%
ValueCountFrequency (%)
55.5131
< 0.1%
59.051
< 0.1%
59.9721
< 0.1%
59.9891
< 0.1%
60.0141
< 0.1%
60.4931
< 0.1%
61.3621
< 0.1%
61.3811
< 0.1%
61.9751
< 0.1%
62.4461
< 0.1%
ValueCountFrequency (%)
215.6691
< 0.1%
210.8931
< 0.1%
207.9821
< 0.1%
205.8461
< 0.1%
205.6631
< 0.1%
205.3751
< 0.1%
204.0021
< 0.1%
203.9271
< 0.1%
203.8621
< 0.1%
202.2831
< 0.1%

duration_ms
Real number (ℝ≥0)

Distinct3025
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219792.6756
Minimum36160
Maximum602297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.3 KiB
2021-10-09T18:35:52.683817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum36160
5-th percentile139777.55
Q1184732.25
median216006.5
Q3246967.5
95-th percentile314783.2
Maximum602297
Range566137
Interquartile range (IQR)62235.25

Descriptive statistics

Standard deviation56271.98434
Coefficient of variation (CV)0.2560230189
Kurtosis4.423107406
Mean219792.6756
Median Absolute Deviation (MAD)31050.5
Skewness1.101923672
Sum707292830
Variance3166536222
MonotonicityNot monotonic
2021-10-09T18:35:52.779399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2041873
 
0.1%
2202933
 
0.1%
2278803
 
0.1%
2165203
 
0.1%
2371073
 
0.1%
1780003
 
0.1%
2178673
 
0.1%
2061203
 
0.1%
2119203
 
0.1%
600002
 
0.1%
Other values (3015)3189
99.1%
ValueCountFrequency (%)
361601
< 0.1%
492421
< 0.1%
554131
< 0.1%
600002
0.1%
657871
< 0.1%
667861
< 0.1%
685861
< 0.1%
720801
< 0.1%
738131
< 0.1%
758001
< 0.1%
ValueCountFrequency (%)
6022971
< 0.1%
5929201
< 0.1%
5763461
< 0.1%
5477331
< 0.1%
5302531
< 0.1%
5263871
< 0.1%
5162931
< 0.1%
5009601
< 0.1%
4961731
< 0.1%
4828301
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size182.4 KiB
4
3041 
3
 
107
5
 
55
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3218
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Length

2021-10-09T18:35:52.970062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-09T18:35:53.027612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Most occurring characters

ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3218
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common3218
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43041
94.5%
3107
 
3.3%
555
 
1.7%
115
 
0.5%

Interactions

2021-10-09T18:35:36.606651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:36.703112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:36.790688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:36.880765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:36.969341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.056416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.147994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.238072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.327648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.416725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.514808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.608890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.696966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.785541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.875618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:37.965195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.053272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.144849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.237429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.328007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.418084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.629265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.724347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.814424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.905001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:38.999082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.092162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.182740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.275820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.372403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.466984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.563566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.666154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.764238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.855818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:39.947897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.043478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.137559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.228137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.320716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.414797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.506876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.600456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.701543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.798126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.886202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:40.974777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.066856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.161438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.254517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.353102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.450686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.542765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.635844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.733929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.826508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:41.920589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.013668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.246868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.343952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.439034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.535616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.633200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.732786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.834873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:42.948972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.047556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.140136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.233215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.327796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.421377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.512454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.610539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.708623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.804205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:43.899286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.006378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.106464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.199044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.290622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.385204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.480786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.574365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.670949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.768032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.862112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:44.956193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.061283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.162370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.256951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.349531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.443611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.538192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.629271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.723351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.821935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:45.917518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.011099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.115187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.213772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.316860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.418447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.689180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.792768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:46.895858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.006452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.113043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.221137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.325726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.441325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.554922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.667018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.765102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.864688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:47.961272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.056352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.156939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.259027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.362115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.464202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-09T18:35:48.577300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-09T18:35:53.106180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-09T18:35:53.284332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-09T18:35:53.461484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-09T18:35:53.643641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-10-09T18:35:53.802777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-10-09T18:35:48.772466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-09T18:35:49.063694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idtitleall_artistspopularityrelease_datedanceabilityenergykeyloudnessmodeacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
00Li1OuXXfi7950ilZUFGkFCardiganDon Toliver702020-03-130.7470.4974-6.54200.24800.0000000.13800.0663142.0461587094
14at3d5QWnlibMVN75ECDrpPretty Little Fears (feat. J. Cole)J. Cole742018-09-140.6100.4589-11.33610.69600.0001030.08520.2460192.0992403424
26b2oQwSGFkzsMtQruIWm2pCreepRadiohead101993-02-220.5150.4307-9.93510.01020.0001410.12900.104091.8412386404
35fsjS6L83RSJBqRJKL0BTYGot Your Money (feat. Kelis)Kelis020080.9320.49910-5.95700.04760.0000000.12100.6970103.0492392934
47cvkXf3AwPGT041PyOi5VXDance for YouBeyoncé622011-06-240.6160.7802-6.06210.03220.0000000.17300.4710110.0053774674
51SGt65i9AnXYdDQt1AtDRH3500 (feat. Future & 2 Chainz)2 Chainz662015-09-040.7730.5958-6.06200.11400.0000020.13000.3380123.9684618404
60EHR9OObFtjlhQB8wSt1m7Mrs. OfficerKidd Kidd652008-06-100.9200.5536-5.06810.23600.0000000.07670.9470112.0222869075
71HDaPtZuixue2q6VGNRdVOPound Cake / Paris Morton Music 2JAY-Z02013-01-010.5210.7622-6.75010.13900.0000110.11400.2640164.2894338004
80kN3oXYWWAk1uC0y2WoyOELove StoryTaylor Swift522008-11-110.6180.7362-3.93710.15700.0000000.07300.3070118.9822352804
96kPJZM97LwdG9QIsT7khp6Solo (feat. Demi Lovato)Demi Lovato732018-05-170.7370.63611-4.54600.04410.0000670.35000.5650105.0052226534

Last rows

idtitleall_artistspopularityrelease_datedanceabilityenergykeyloudnessmodeacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signature
32085jbP56kEW0UPoGBICpl7VTBlinding Lights - Chromatics RemixJohnny Jewel02020-04-030.6180.6548-6.09110.00840.0001000.06470.712171.0322612404
32092VOomzT6VavJOGBeySqaMcDisturbiaRihanna762008-06-020.7070.81311-4.51500.08630.0000000.16800.722124.9212386274
32101xpXnpIpuvzpx9jz15baJFCall MeMetro Boomin642017-07-210.6780.3959-8.63900.41100.0054400.12200.136170.0652150674
32115VA4Ispp52EA1sOqzMz3AvAll I Want Is You (feat. J. Cole)J. Cole642010-11-260.7760.6035-5.69610.07280.0000630.11100.92196.8882959204
32125YSfZJE0EYrGVsWeYqv9xoDouble Trouble (Quavo feat. Meek Mill)Meek Mill512019-08-160.8680.6612-3.67010.07690.0000070.11400.482165.0201769114
32130nbXyq5TXYPCO7pr3N8S4IThe BoxRoddy Ricch832019-12-060.8960.58610-6.68700.10400.0000000.79000.642116.9711966534
32141xNQsOODPDIcwbeVqKzUt6TR666 (Freestyle)DJ Max Star02018-10-040.6070.46711-11.47500.43300.0004190.11400.162170.0611544114
32151s2GBzXhabhK1bjGFGMqx0Car Wash (Shark Tale Mix)Missy Elliott02004-01-010.7940.8180-4.24000.15200.0000050.22100.927116.0532286674
32165TImISmK9CQtBrt0rLbq0RHelp YourselfAmy Winehouse4820030.7820.4816-6.68400.03860.0007020.03640.72888.0113010004
32172EniaQpyAdPbOt6irqXSpNPoppinRico Nasty462017-10-240.7590.7400-4.84710.11500.0000000.26300.203140.0521670914